基因表达谱中信号通路基因集分析方法进展

微阵列技术是生物技术变革的核心,允许研究者同时监测成千上万个基的表达水平,已广泛应用医学研究.如何挖掘海量基表达信息中的有用信息并进行生物学专业解释,是基表达谱数据分析领所面临的一个重要挑战.生物信号通路研究已成为基芯片中不同表型差异表达研究的主要方法,其是以整个信号通路作为一个整体作为研究对象,此得出的研究结果更加科学和准确.在本文中我们简要描述了近10年来信号通路基集富集分析方法的发展情况,将其分为三个阶段,对每个阶段方法的基础和特点做了一些简单的总结和阐述.     

GAGE基因家族的分子进化特征的研究

GAGE基因通常表达于睾丸组织和部分恶性肿瘤组织中, 被认为可能是理想的癌症诊断的标记和治疗的靶位。我们对GAGE基因家族作了生物信息学分析, 发现它们在X染色体上串联成簇排列, 为灵长类所独有, 各拷贝序列趋异度很低。在人类有15个以上的拷贝, 在黑猩猩和猕猴分别有3个和4个。对GAGE基因家族构建进化树, 并估算了复制事件发生的时间, 结果显示在近400万年内陆续发生。用两种方法计算了GAGE各拷贝间的Ka/Ks值, 结果为显著大于1, 表明该基因家族受到正选择作用。这些结果提示该基因可能与灵长类的特征有关, 其在进化上的地位和在配子发育和肿瘤发生中承担的功能值得深入研究。     

GSEA在全基因组表达谱芯片数据分析中的应用

GSEA是一个可下载后免费使用的全基因组表达谱芯片数据分析工具。它根据已有的对基因的定位、性质、功能、生物学意义等知识的基础上,首先构建了一个分子标签数据库,数据库中包含了多个功能基因集。通过分析一组处于两个生物学状态的基因表达谱杂交数据,它们在特定的功能基因集中的表达状况,以及这种表达状况是否存在某种统计学显著性。GSEA是从另一个角度来诠释生物信息,可进一步完善我们对相关生物学事件的认识。     

基因表达谱富集分析方法研究进展

微阵列技术是生物技术变革的核心,允许研究者同时监测成千上万个基因的表达水平,已广泛应用于医学研究。如何挖掘海量基因表达信息中的有用信息并进行生物学专业解释,是基因表达谱数据分析领域所面临的一个重要挑战。不同的研究者提出了各种基于基因集进行富集分析的方法,在此将这些方法大致分为两大类,即bottom-up方法和top-down方法。前者先进行单基因分析,然后根据生物学领域知识注释基因集并进行分析。该方法应用广泛,且结果比单基因分析容易解释。后者先根据生物学领域知识将各基因进行归类,然后进行基因差异表达模式分析。该方法不仅能提高结论的可解释性,而且能达到降维的目的。     

改进型基因集测试在乳腺癌研究中的应用

乳腺癌是一种异源性疾病,包括至少5到6种分子亚型.在正常乳腺细胞中寻找不同癌症亚型的细胞起源非常重要,但很难做到。基因集测试(Genesettest)是处理微列阵(microarray)数据的常用生物统计方法,包括传统型和通用型。通用型基因集测试又分为竞争型和自含型。墨尔本大学的科学家用改进的基因集测试:修正竞争型测试(CAMERA)和旋转基因集测试(ROAST)的方法分析了乳腺癌亚型与乳腺细胞间的对应相似性,发现正常内腔鲁米那前体细胞很可能是基底型乳腺癌的细胞来源。此项研究成为澳大利亚年度生物医学的重大成果。     

基于基因表达谱的肿瘤分型和特征基因选取

在分析基因表达谱数据特性的基础上,提出了一个将之用于肿瘤分子分型和选型和选取相应亚型特征基因的策略。该策略包括三个步骤：首先采用一个无监督的基因过滤算法以降低用于分型计算的数据的噪声,其次提出了一个概率模型对样本中的分类结构进行建模,最后基于聚类的结果采用相对熵的方法获得对分类贡献大的基因作为特征基因,应用该策略对两个公开发表的数据集进行了再挖掘,结果表明不但获得了其他方法可以得到的信息,而且还提供了更精细、更具有显著生物学意义的信息,具有明显的优越性。     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

分子伴侣 SecB 基因和人淋巴毒素基因在大肠杆菌中的共表达

分子伴侣ＳｅｃＢ基因和人淋巴毒素基因在大肠杆菌中的共表达周颖张青殷长传宋大新陈永青（复旦大学微生物学系和遗传研究所上海２００４３３）分子伴侣（Ｃｈａｐｅｒｏｎｅ）是细胞内催化及维持其他蛋白质正确构象的一类蛋白质分子［１,２］。研究表明,分子伴...     

基于蒙特卡洛模拟分析不同基因集方法的效能

目的:探索差异表达基因集(DEGs)筛选的有效方法.方法:基于蒙特卡洛模拟比较Efron's GSA、SAFE、Globaltest、PCOT2等四种基因集方法在分析微阵列数据时的统计推断能力.结果:Globaltest和PCOT2两种基于模型构建的基因集方法在处理模拟微阵列数据时效能相当,Globaltest略优于PCOT2,而Effort's GSA、SAFE方法检验效能低下.结论:Globaltest是一种较有效的微阵列数据分析方法.     

Gene-set analysis and reduction

Gene-set analysis aims to identify differentially expressedgene sets (pathways) by a phenotype in DNA microarray studies.We review here important methodological aspects of gene-setanalysis and illustrate them with varying performance of severalmethods proposed in the literature. We emphasize the importanceof distinguishing between ‘self-contained’ versus‘competitive’ methods, following Goeman and Bühlmann.We also discuss reducing a gene set to its subset, consistingof ‘core members’ that chiefly contribute to thestatistical significance of the differential expression of theinitial gene set by phenotype. Significance analysis of microarrayfor gene-set reduction (SAM-GSR) can be used for an analyticalreduction of gene sets to their core subsets. We apply SAM-GSRon a microarray dataset for identifying biological gene sets(pathways) whose gene expressions are associated with p53 mutationin cancer cell lines. Codes to implement SAM-GSR in the statisticalpackage R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.      

Genes Selection Comparative Study in Microarray Data Analysis

In response to the rapid development of DNA Microarray Technologies, many differentially expressed genes selection algorithms have been developed, and different comparison studies of these algorithms have been done. However, it is not clear how these methods compare with each other, especially when we used different developments tools. Here, we considered three commonly used differentially expressed genes selection approaches, namely: Fold Change, T-test and SAM, using Bioinformatics Matlab Toolbox and R/BioConductor. We used two datasets, issued from the affymetrix technology, to present results of used methods and software''s in gene selection process. The results, in terms of sensitivity and specificity, indicate that the behavior of SAM is better compared to Fold Change and T-test using R/BioConductor. While, no practical differences were observed between the three gene selection methods when using Bioinformatics Matlab Toolbox. In face of our result, the ROC curve shows that: on the one hand R/BioConductor using SAM is favored for microarray selection compared to the other methods. And, on the other hand, results of the three studied gene selection methods using Bioinformatics Matlab Toolbox are still comparable for the two datasets used.     

Gene Set Enrichment Analysis (GSEA) of Toxoplasma gondii expression datasets links cell cycle progression and the bradyzoite developmental program

Background

Large amounts of microarray expression data have been generated for the Apicomplexan parasite Toxoplasma gondii in an effort to identify genes critical for virulence or developmental transitions. However, researchers’ ability to analyze this data is limited by the large number of unannotated genes, including many that appear to be conserved hypothetical proteins restricted to Apicomplexa. Further, differential expression of individual genes is not always informative and often relies on investigators to draw big-picture inferences without the benefit of context. We hypothesized that customization of gene set enrichment analysis (GSEA) to T. gondii would enable us to rigorously test whether groups of genes serving a common biological function are co-regulated during the developmental transition to the latent bradyzoite form.

Results

Using publicly available T. gondii expression microarray data, we created Toxoplasma gene sets related to bradyzoite differentiation, oocyst sporulation, and the cell cycle. We supplemented these with lists of genes derived from community annotation efforts that identified contents of the parasite-specific organelles, rhoptries, micronemes, dense granules, and the apicoplast. Finally, we created gene sets based on metabolic pathways annotated in the KEGG database and Gene Ontology terms associated with gene annotations available at http://www.toxodb.org. These gene sets were used to perform GSEA analysis using two sets of published T. gondii expression data that characterized T. gondii stress response and differentiation to the latent bradyzoite form.

Conclusions

GSEA provides evidence that cell cycle regulation and bradyzoite differentiation are coupled. Δgcn5A mutants unable to induce bradyzoite-associated genes in response to alkaline stress have different patterns of cell cycle and bradyzoite gene expression from stressed wild-type parasites. Extracellular tachyzoites resemble a transitional state that differs in gene expression from both replicating intracellular tachyzoites and in vitro bradyzoites by expressing genes that are enriched in bradyzoites as well as genes that are associated with the G1 phase of the cell cycle. The gene sets we have created are readily modified to reflect ongoing research and will aid researchers’ ability to use a knowledge-based approach to data analysis facilitating the development of new insights into the intricate biology of Toxoplasma gondii.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-515) contains supplementary material, which is available to authorized users.     

Gene-set based genome-wide association analysis for the speed of sound in two skeletal sites of Korean women

The speed of sound (SOS) value is an indicator of bone mineral density (BMD). Previous genome-wide association (GWA) studies have identified a number of genes, whose variations may affect BMD levels. However, their biological implications have been elusive. We re-analyzed the GWA study dataset for the SOS values in skeletal sites of 4,659 Korean women, using a gene-set analysis software, GSA-SNP. We identified 10 common representative GO terms, and 17 candidate genes between these two traits (P_GS ＜ 0.05). Implication of these GO terms and genes in the bone mechanism is well supported by the literature survey. Interestingly, the significance levels of some member genes were inversely related, in several gene-sets that were shared between two skeletal sites. This implies that biological process, rather than SNP or gene, is the substantial unit of genetic association for SOS in bone. In conclusion, our findings may provide new insights into the biological mechanisms for BMD. [BMB Reports 2014; 47(6): 348-353]     

Comparative Study of Microsporidian Spores by Flow Cytometric Analysis

ABSTRACT. Spore suspensions of microsporidian parasites of fish (Microsporidium ovoideum, Glugea stephani, Glugea atherinae and Spraguea lophii ) have been analyzed by flow cytometry. Spore nuclei were dyed either by propidium iodide or bis-benzimide (Hoechst 33342). By observation of forward light scatter and fluorescence the four species could be distinguished and the mono- and diplokaryotic populations of S. lophii identified. Staining of DNA by bis-benzimide was better and easier than propidium iodide. Forward light scatter and fluorescence values were characteristic of each species and remained unchanged throughout the year, so flow cytometry can be used for distinction of spores of some microsporidian parasites once their flow cytometric parameters are known. However, special care has to be taken in tool calibration and material preparation for analysis because of the high precision of the technique.     

Reevaluating Assembly Evaluations with Feature Response Curves: GAGE and Assemblathons

In just the last decade, a multitude of bio-technologies and software pipelines have emerged to revolutionize genomics. To further their central goal, they aim to accelerate and improve the quality of de novo whole-genome assembly starting from short DNA sequences/reads. However, the performance of each of these tools is contingent on the length and quality of the sequencing data, the structure and complexity of the genome sequence, and the resolution and quality of long-range information. Furthermore, in the absence of any metric that captures the most fundamental “features” of a high-quality assembly, there is no obvious recipe for users to select the most desirable assembler/assembly. This situation has prompted the scientific community to rely on crowd-sourcing through international competitions, such as Assemblathons or GAGE, with the intention of identifying the best assembler(s) and their features. Somewhat circuitously, the only available approach to gauge de novo assemblies and assemblers relies solely on the availability of a high-quality fully assembled reference genome sequence. Still worse, reference-guided evaluations are often both difficult to analyze, leading to conclusions that are difficult to interpret. In this paper, we circumvent many of these issues by relying upon a tool, dubbed , which is capable of evaluating de novo assemblies from the read-layouts even when no reference exists. We extend the FRCurve approach to cases where lay-out information may have been obscured, as is true in many deBruijn-graph-based algorithms. As a by-product, FRCurve now expands its applicability to a much wider class of assemblers – thus, identifying higher-quality members of this group, their inter-relations as well as sensitivity to carefully selected features, with or without the support of a reference sequence or layout for the reads. The paper concludes by reevaluating several recently conducted assembly competitions and the datasets that have resulted from them.     

Comparative Study and Mutational Analysis of Distinctive Structural Elements of Hyperthermophilic Enzymes

Comparison of the three-dimensional structure of hyperthermophilic and mesophilic β-glycosidases shows differences in secondary structure composition. The enzymes from hyperthermophilic archaea have a significantly larger number of β-strands arranged in supernumerary β-sheets compared to mesophilic enzymes from bacteria and other organisms. Amino acid replacements designed to alter the structure of the supernumerary β-strands were introduced by site directed mutagenesis into the sequence encoding the β-glycosidase from Sulfolobus solfataricus. Most of the replacements caused almost complete loss of activity but some yielded enzyme variants whose activities were affected specifically at higher temperatures. Far-UV CD spectra recorded as a function of temperature for both wild type β-glycosidase and mutant V349G, one of the mutants with reduced activity at higher temperatures, were similar, showing that the protein structure of the mutant was stable at the highest temperatures assayed. The properties of mutant V349G show a difference between thermostability (stability of the protein structure at high temperatures) and thermophilicity (optimal activity at high temperatures).